Overview

Welcome to the ggplot2 graph gallery! This Quarto website contains a series of graphs composed in ggplot2.

Install ggplot2 using the code below:

View code

install.packages("ggplot2")
library(ggplot2)

Or install ggplot2 as part of the tidyverse:

View code

install.packages("tidyverse")
library(tidyverse)

Data

So many penguins!

To improve reproducibility, the majority of the graphs are built using the palmerpenguins::penguins data.

`penguins`

Artwork by @allison_horst

Install this package using the code below:

penguins data

View code

install.packages("palmerpenguins")
library(palmerpenguins)
palmerpenguins::penguins |> glimpse()

Rows: 344
Columns: 8
$ species           <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…
$ island            <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…
$ bill_length_mm    <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …
$ bill_depth_mm     <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …
$ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…
$ body_mass_g       <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …
$ sex               <fct> male, female, female, NA, female, male, female, male…
$ year              <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…

Some of the graphs use datasets from the fivethirtyeight package.

fivethirtyeight data

To view a table of available datasets in the fivethirtyeight package, view the Data Frame Name and Article Title columns in the datasets_master table.

View code

install.packages("fivethirtyeight")
library(fivethirtyeight)
fivethirtyeight::datasets_master |> 
    select(`Data Frame Name`, `Article Title`)

Additional datasets

`movies`

A few of the graphs are built using the ggplot2movies::movies data.

movies data

View code

install.packages("ggplot2movies")
library(ggplot2movies)
ggplot2movies::movies |> glimpse()

Rows: 58,788
Columns: 24
$ title       <chr> "$", "$1000 a Touchdown", "$21 a Day Once a Month", "$40,0…
$ year        <int> 1971, 1939, 1941, 1996, 1975, 2000, 2002, 2002, 1987, 1917…
$ length      <int> 121, 71, 7, 70, 71, 91, 93, 25, 97, 61, 99, 96, 10, 10, 10…
$ budget      <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ rating      <dbl> 6.4, 6.0, 8.2, 8.2, 3.4, 4.3, 5.3, 6.7, 6.6, 6.0, 5.4, 5.9…
$ votes       <int> 348, 20, 5, 6, 17, 45, 200, 24, 18, 51, 23, 53, 44, 11, 12…
$ r1          <dbl> 4.5, 0.0, 0.0, 14.5, 24.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4…
$ r2          <dbl> 4.5, 14.5, 0.0, 0.0, 4.5, 4.5, 0.0, 4.5, 4.5, 0.0, 0.0, 0.…
$ r3          <dbl> 4.5, 4.5, 0.0, 0.0, 0.0, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5…
$ r4          <dbl> 4.5, 24.5, 0.0, 0.0, 14.5, 14.5, 4.5, 4.5, 0.0, 4.5, 14.5,…
$ r5          <dbl> 14.5, 14.5, 0.0, 0.0, 14.5, 14.5, 24.5, 4.5, 0.0, 4.5, 24.…
$ r6          <dbl> 24.5, 14.5, 24.5, 0.0, 4.5, 14.5, 24.5, 14.5, 0.0, 44.5, 4…
$ r7          <dbl> 24.5, 14.5, 0.0, 0.0, 0.0, 4.5, 14.5, 14.5, 34.5, 14.5, 24…
$ r8          <dbl> 14.5, 4.5, 44.5, 0.0, 0.0, 4.5, 4.5, 14.5, 14.5, 4.5, 4.5,…
$ r9          <dbl> 4.5, 4.5, 24.5, 34.5, 0.0, 14.5, 4.5, 4.5, 4.5, 4.5, 14.5,…
$ r10         <dbl> 4.5, 14.5, 24.5, 45.5, 24.5, 14.5, 14.5, 14.5, 24.5, 4.5, …
$ mpaa        <chr> "", "", "", "", "", "", "R", "", "", "", "", "", "", "", "…
$ Action      <int> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0…
$ Animation   <int> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1…
$ Comedy      <int> 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1…
$ Drama       <int> 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0…
$ Documentary <int> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0…
$ Romance     <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ Short       <int> 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1…

Code Style & Structure

The gallery follows a Rule of Least Power Principle, in the sense that “a language with a straightforward syntax may be easier to analyze than an otherwise equivalent one with more complex structure.” In other words, assuming the reader has some understanding of R and the tidyverse, the code for each graph is meant to be read and understood without having to run it.

All graph labels have the labs_ prefix
All graph layers have a ggp2_ prefix

Each graph has the same four sections and structure: Packages, Data, Code, and Graph

Graph Code Structure

Packages
Data

PACKAGES:

Install packages.

View code

library(ggplot2)

DATA:

Description of data

View code

df <- tibble::tibble(X = sample(x = 1:100, 10, FALSE),
                     Y = rlnorm(10, 1, 3))

Code
Graph

CODE:

Code for creating graph

View code

# labels
labs_graph <- ggplot2::labs(title = "Title", 
                            subtitle = "subtitle", 
                            x = "X", y = "Y")
# layers
ggp2_graph <- ggplot2::ggplot(data = df, 
    mapping = aes(x = X, y = Y)) + 
    ggplot2::geom_blank()
# graph
ggp2_graph + 
    labs_graph

GRAPH:

I’ve attempted to balance brevity and clarity, but with the assumption that its best to err on the latter. I’ve also followed the general principle that if a graph can be easily built using one of ggplot2 ’s geom_* functions, that method is shown first.

If the graph can’t be built with ggplot2’s geoms and requires additional packages/geoms/functions, instructions for installing the development version for these packages has been provided.

Graph Categories

Graphs have been categorized into the following types:

Univariate graphs
Amounts
Proportions
Comparing Distributions
Relationships

Some graphs would justifiably belong in more than one category, and wherever this is the cases, I’ve tried to include other uses in the notes.

Amounts

The following graphs are used to display amounts:

Grouped column-graphs
Summarized column-graphs
Overlapping bar-graphs
Side-to-side ("dodge") bar-graphs
Side-to-side ("dodge2") bar-graphs

Waffle charts

ggwaffle package

View code

# Waffle Charts
devtools::install_github("liamgilbey/ggwaffle")
library(ggwaffle)

Heatmaps

Proportions

The following graphs are used to compare proportions:

Pie Charts

ggpubr package

View code

# Pie Charts 
devtools::install_github("kassambara/ggpubr")
library(ggpubr)

Diverging Bar-graphs
Vertical
Stacked-densities

Mosaic Plots

ggmosaic package

View code

# Mosaic Charts 
devtools::install_github("haleyjeppson/ggmosaic")
library(ggmosaic)

Treemaps

treemapify package

View code

# Treemaps
devtools::install_github("wilkox/treemapify")
library(treemapify)

Comparing Distributions

These graphs can be used to compare multiple variable distributions:

Overlapping Histograms
Overlapping Frequency Polygons
Overlapping Dot-Plots

Bee-swarm Plots

ggbeeswarm package

View code

# Bee-swarm Plots
devtools::install_github("eclarke/ggbeeswarm")
library(ggbeeswarm)

Overlapping Densities

Ridgeline Plots

ggridges package

View code

# Ridgeline plots 
devtools::install_github("wilkelab/ggridges")
library(ggridges)

Grouped Box-plots
Violin plots

Rain-cloud plots

raincloudplots and ggdist packages

View code

# Rain-cloud plots 
remotes::install_github('jorvlan/raincloudplots')
remotes::install_github('mjskay/ggdist')
library(raincloudplots)
library(ggdist)

Relationships

The following graphs are used to display relationships between two (or more) variables:

Scatter plots
Grouped scatter plots
Bubble charts
Grouped bubble charts

Alluvial charts

ggalluvial package

View code

# Alluvial charts
devtools::install_github("corybrunson/ggalluvial")
library(ggalluvial)

Bump charts

ggbump package

View code

# Bump charts
devtools::install_github("davidsjoberg/ggbump")
library(ggbump)

Parallel Sets

ggforce package

View code

# Bump charts
devtools::install_github("thomasp85/ggforce")
library(ggforce)

Slope graphs
Density Contours
2D Histograms
Hex Bins
Correlograms

--- title: "Overview" format: html: toc: true toc-depth: 3 embed-resources: true smooth-scroll: true code-fold: show code-summary: "View code" code-overflow: wrap highlight-style: arrow --- quarto-executable-code-5450563D ```r #| label: setup #| message: false #| warning: false #| include: false library(tidyverse) library(lubridate) library(scales) library(knitr) library(kableExtra) library(colorblindr) library(downlit) # options ---- options( repos = "https://cloud.r-project.org", dplyr.print_min = 6, dplyr.print_max = 6, scipen = 9999) ggplot2::theme_set(ggplot2::theme_minimal(base_size = 14)) ``` Welcome to the `ggplot2` graph gallery! This Quarto website contains a series of graphs composed in `ggplot2`. ::: {.column-margin} ![](www/ggplot2.png){fig-align="center" width="40%" height="40%"} ::: Install `ggplot2` using the code below: quarto-executable-code-5450563D ```r #| label: ggp2-pkg #| eval: false #| echo: true #| code-fold: show install.packages("ggplot2") library(ggplot2) ``` Or install `ggplot2` as part of the `tidyverse`: ::: {.column-margin} ![](www/tidyverse.png){fig-align="center" width="40%" height="40%"} ::: quarto-executable-code-5450563D ```r #| label: tidyverse-pkg #| eval: false #| echo: true #| code-fold: show install.packages("tidyverse") library(tidyverse) ``` ## Data ::: {style="font-size: 1.25em; color: #38B44A;"} ***So many penguins!*** ::: To improve reproducibility, the majority of the graphs are built using the `palmerpenguins::penguins` data. ### `penguins` ::: {.column-margin} ![Artwork by @allison_horst](www/lter_penguins.png){fig-align="center" width="50%" height="50%"} ::: Install this package using the code below: :::: {.callout-note collapse="true"} ## `penguins` data quarto-executable-code-5450563D ```r #| label: penguins-pkg-show #| eval: false #| echo: true install.packages("palmerpenguins") library(palmerpenguins) palmerpenguins::penguins |> glimpse() ``` quarto-executable-code-5450563D ```r #| label: penguins-pkg #| eval: true #| echo: false #| message: false #| warning: false library(palmerpenguins) palmerpenguins::penguins |> glimpse() ``` ::: Some of the graphs use datasets from the `fivethirtyeight` package. ::: {.column-margin} ![](www/538.png){fig-align="center" width="30%" height="30%"} ::: :::: {.callout-note collapse="true"} ## `fivethirtyeight` data To view a table of available datasets in the `fivethirtyeight` package, view the `Data Frame Name` and `Article Title` columns in the `datasets_master` table. quarto-executable-code-5450563D ```r #| label: 538-pkg-show #| eval: false #| echo: true install.packages("fivethirtyeight") library(fivethirtyeight) fivethirtyeight::datasets_master |> select(`Data Frame Name`, `Article Title`) ``` quarto-executable-code-5450563D ```r #| label: 538-pkg #| eval: true #| echo: false #| message: false #| warning: false library(fivethirtyeight) reactable::reactable(fivethirtyeight::datasets_master |> select(`Data Frame Name`, `Article Title`), defaultPageSize = 5) ``` ::: ### Additional datasets ### `movies` ::: {.column-margin} ![](www/imdb.png){fig-align="center" width="30%" height="30%"} ::: A *few* of the graphs are built using the `ggplot2movies::movies` data. :::: {.callout-note collapse="true"} ## `movies` data quarto-executable-code-5450563D ```r #| label: movies-pkg-show #| eval: false #| echo: true install.packages("ggplot2movies") library(ggplot2movies) ggplot2movies::movies |> glimpse() ``` quarto-executable-code-5450563D ```r #| label: movies-pkg #| eval: true #| echo: false #| message: false #| warning: false library(ggplot2movies) ggplot2movies::movies |> glimpse() ``` ::: ## Code Style & Structure The gallery follows a [Rule of Least Power Principle](https://www.w3.org/2001/tag/doc/leastPower.html), in the sense that "*a language with a straightforward syntax may be easier to analyze than an otherwise equivalent one with more complex structure.*" In other words, assuming the reader has some understanding of R and the `tidyverse`, the code for each graph is meant to be read and understood *without* having to run it. ::: {style="font-size: 0.95em; color: #02577A;"} - **All graph labels have the `labs_` prefix** - **All graph layers have a `ggp2_` prefix** ::: Each graph has the same four sections and structure: **Packages**, **Data**, **Code**, and **Graph** ::::: {.callout-note collapse="false"} ### Graph Code Structure :::: {.panel-tabset} ### Packages ::: {style="font-size: 0.90em; color: #1e83c8;"} **PACKAGES:** ::: ::: {style="font-size: 0.85em;"} Install packages. ::: ::: {style="font-size: 0.75em;"} quarto-executable-code-5450563D ```r #| label: pkg_code_example #| eval: false #| echo: true #| warning: false #| message: false #| results: hide library(ggplot2) ``` ::: ### Data ::: {style="font-size: 0.90em; color: #1e83c8;"} **DATA:** :::  ::: {style="font-size: 0.85em;"} Description of data ::: ::: {style="font-size: 0.75em;"} quarto-executable-code-5450563D ```r #| label: data_code_example #| eval: true #| echo: true df <- tibble::tibble(X = sample(x = 1:100, 10, FALSE), Y = rlnorm(10, 1, 3)) ``` ::: :::: :::: {.panel-tabset} ### Code ::: {style="font-size: 0.90em; color: #1e83c8;"} **CODE:** ::: ::: {style="font-size: 0.85em;"} Code for creating graph ::: ::: {style="font-size: 0.75em;"} quarto-executable-code-5450563D ```r #| label: code_graph_example #| eval: false #| echo: true #| warning: false #| message: false # labels labs_graph <- ggplot2::labs(title = "Title", subtitle = "subtitle", x = "X", y = "Y") # layers ggp2_graph <- ggplot2::ggplot(data = df, mapping = aes(x = X, y = Y)) + ggplot2::geom_blank() # graph ggp2_graph + labs_graph ``` ::: ### Graph ::: {style="font-size: 0.90em; color: #1e83c8;"} **GRAPH:** ::: quarto-executable-code-5450563D ```r #| label: create_graph_example #| eval: true #| echo: false #| warning: false #| message: false # labels labs_graph <- ggplot2::labs(title = "Title", subtitle = "subtitle", x = "X", y = "Y") # layers ggp2_graph <- ggplot2::ggplot(data = df, mapping = aes(x = X, y = Y)) + ggplot2::geom_blank() # graph ggp2_graph + labs_graph ``` :::: ::::: I've attempted to balance brevity and clarity, but with the assumption that its best to err on the latter. I've also followed the general principle that if a graph can be *easily* built using one of `ggplot2` 's `geom_*` functions, that method is shown first. If the graph can't be built with `ggplot2`'s geoms and requires additional packages/geoms/functions, instructions for installing the development version for these packages has been provided. ## Graph Categories Graphs have been categorized into the following types: - Univariate graphs - Amounts - Proportions - Comparing Distributions - Relationships Some graphs would justifiably belong in more than one category, and wherever this is the cases, I've tried to include other uses in the notes. ### Univariate Graphs The following graphs are used to display **single variable** **distributions**: - [x] Bar Graphs - [x] Histograms - [x] Frequency Polygons - [x] Dot-Plots - [x] Density Plots - [x] Violin Plots - [x] Box-Plots ### Amounts The following graphs are used to display **amounts**: - [x] Grouped column-graphs - [x] Summarized column-graphs - [x] Overlapping bar-graphs - [x] Side-to-side (`"dodge"`) bar-graphs - [x] Side-to-side (`"dodge2"`) bar-graphs - [x] Waffle charts - [`ggwaffle` package](https://liamgilbey.github.io/ggwaffle/) ```{r} #| eval: false #| echo: true # Waffle Charts devtools::install_github("liamgilbey/ggwaffle") library(ggwaffle) ``` - [x] Heatmaps ### Proportions The following graphs are used to compare **proportions**: - [x] Pie Charts - [`ggpubr` package](https://rpkgs.datanovia.com/ggpubr/) ```{r} #| eval: false #| echo: true # Pie Charts devtools::install_github("kassambara/ggpubr") library(ggpubr) ``` - [x] Diverging Bar-graphs - [x] Vertical - [x] Stacked-densities - [x] Mosaic Plots - [`ggmosaic` package](https://haleyjeppson.github.io/ggmosaic/) ```{r} #| eval: false #| echo: true # Mosaic Charts devtools::install_github("haleyjeppson/ggmosaic") library(ggmosaic) ``` - [x] Treemaps - [`treemapify` package](https://wilkox.org/treemapify/) ```{r} #| eval: false #| echo: true # Treemaps devtools::install_github("wilkox/treemapify") library(treemapify) ``` ### Comparing Distributions These graphs can be used to compare **multiple variable distributions:** - [x] Overlapping Histograms - [x] Overlapping Frequency Polygons - [x] Overlapping Dot-Plots - [x] Bee-swarm Plots - [`ggbeeswarm` package](https://github.com/eclarke/ggbeeswarm) ```{r} #| eval: false #| echo: true # Bee-swarm Plots devtools::install_github("eclarke/ggbeeswarm") library(ggbeeswarm) ``` - [x] Overlapping Densities - [x] Ridgeline Plots - [`ggridges` package](https://wilkelab.org/ggridges/) ```{r} #| eval: false #| echo: true # Ridgeline plots devtools::install_github("wilkelab/ggridges") library(ggridges) ``` - [x] Grouped Box-plots - [x] Violin plots - [x] Rain-cloud plots - [`raincloudplots`](https://github.com/jorvlan/raincloudplots) and [`ggdist`](https://mjskay.github.io/ggdist/reference/index.html) packages ```{r} #| eval: false #| echo: true # Rain-cloud plots remotes::install_github('jorvlan/raincloudplots') remotes::install_github('mjskay/ggdist') library(raincloudplots) library(ggdist) ``` ### Relationships The following graphs are used to display relationships between two (or more) variables: - [x] Scatter plots - [x] Grouped scatter plots - [x] Bubble charts - [x] Grouped bubble charts - [x] Alluvial charts - [ggalluvial package](https://corybrunson.github.io/ggalluvial/) ```{r} #| eval: false #| echo: true # Alluvial charts devtools::install_github("corybrunson/ggalluvial") library(ggalluvial) ``` - [x] Bump charts - [ggbump package](https://github.com/davidsjoberg/ggbump) ```{r} #| eval: false #| echo: true # Bump charts devtools::install_github("davidsjoberg/ggbump") library(ggbump) ``` - [x] Parallel Sets - [ggforce package](https://ggforce.data-imaginist.com/index.html) ```{r} #| eval: false #| echo: true # Bump charts devtools::install_github("thomasp85/ggforce") library(ggforce) ``` - [x] Slope graphs - [x] Density Contours - [x] 2D Histograms - [x] Hex Bins - [ ] *Correlograms*